In the past few years, short video e-commerce mainly operated through the traditional "live shooting + editing" method. But as AI video models like Sora and Runway rapidly mature, this set of rules has also been broken.

Now, more and more merchants are starting to use AI to generate e-commerce videos, not only greatly improving efficiency, but also drastically reducing costs. The entire market rhythm has changed.

Previously, a professional content team producing 30 videos a day was considered efficient. Now, one person using AI tools can batch produce hundreds of videos in just a few hours.

The gap in productivity is so huge that the traditional manual model has almost lost its competitiveness.

When your competitor can test ten times the creative ideas at one-tenth the cost, this competition has already become a contest of "algorithms and computing power".


From“sense of reality” to “sense of trust”

"Native feeling" was once regarded as the standard for content creation. But judging from the data of AI video placements, what users really care about is not whether it was "shot by real people", but whether the content is trustworthy and the product is reliable.

Moreover, from the perspective of platform rules, platforms do not restrict AI creation. What they crack down on are low-quality, false, or exaggerated content. As long as the content is high quality and the promotional material/product is authentic, the algorithm will still give traffic.

For example, recently on TikTok, this store has been quite popular, focusing on the European and American apparel independent site Sheenplace.

 

According to Pipiads data analysis,this apparel independent siteplaced a total of 6,393 ads, with total views reaching 77.9 million.

During Black Friday, their traffic curve almost shot up vertically. There is only one explanation behind this: full AI computing power, instantly flooding the market with hundreds of pieces of material.

 

Data source:pipiads

Their core tactic is one copy, splitting into thousands of videos.

Ad copy:"Wear the Viking Spirit. Conquer Winter with Rugged Style and Unstoppable Courage!"

This single copy is linked to 2,000 ad materials, with 59.3M views.

 

Data source:pipiads

The video content completely abandons traditional live shooting, using AI-generated hyper-realistic static images: silver-haired tough guys, exaggerated muscle lines, complex Celtic tattoos, plus simple dynamic parallax effects to make the picture "come alive".

A single viral video reached 2.7 million views, with a production cost of less than $1.

 

Data source:pipiads

What's smart about this approach? They're not selling the clothes themselves, but a ticket to enter the "tough guy circle".

Movie-level visual quality instantly grabs attention, combined with the call to "Join the Viking Tribe", precisely hitting the target user's need for identity recognition.

This is not creation, it's industrial production. They don't need to go to Northern Europe for location shoots or find models; just a set of precise prompts can drive the cost of visual materials down to rock bottom.

Another example is the anime IP apparel store ComicsSoul, whose data is also impressive.

 

Data source:pipiads

This store has placed a total of 10,600 ads, with recent views reaching 2.7 million. Pipiads data shows their traffic curve presents obvious "sawtooth" peaks—usually maintaining low-intensity testing, suddenly exploding at key moments. This is exactly the flexibility of AI production capacity.

 

Data source:pipiads

Their core gameplay is also very straightforward: a validated copy, paired with thousands of AI visual materials, repeatedly bombarding users.

A single copy brings 1.6 million views, covering Pikachu, Charizard, Dragon Ball Goku and other IPs.

The real killer is the viral video with 90.8K views, only 14 seconds long:

· No real people appear, only "invisible models" showcase the clothes

· Dragon Ball pants prints use "shimmering" glowing effects

· Within 15 seconds, the video switches between Goku, Vegeta, Broly, Buu and 10 other character styles

 

For this kind of visual effect, traditional shooting requires sample clothes, lighting, and special effects teams, which is costly and slow.AI skips all of that and turns the clothes into "digital artworks". What users are paying for is not the physical item, but the IP sense of identity plus the emotional impact brought by the visual spectacle.

 

What changes has AI video brought to business logic?

 

These cases reveal a harsh reality: content creation has shifted from "art" to "engineering".

Traditional teams can produce at most 30 videos a day, requiring 1-2 hours for the director to write the script, 4-6 hours for shooting and lighting, 2-4 hours for editing and special effects—a total of 8-12 hours for one video. But an AI team only needs 30 minutes to write the copy strategy, and 2 hours to generate 200 videos, improving efficiency by 66 times.

The most fatal gap is in the speed of trial and error. While you are repeatedly revising one video, your competitor has already used AI to generate 50 different versions, repeatedly targeting your potential customers. More importantly, Pipiads data shows that 99% of these AI ads are not flagged by the platform. The platform only rejects low-quality content, not AI; as long as the lighting is realistic and the movements are natural, the algorithm can't tell the difference between live shooting and AI generation.

 

What truly changes the game is the complete restructuring of the team

Previously, you needed a team of more than 10 people: director, photographer, lighting technician, model, editor, etc. Now, you only need a copy strategist to accurately understand user psychology, an AI operator to turn business logic into replicable templates, and a data analyst to use Pipiads to track ROI and guide iteration. Three people can match the output of a traditional 30-person team, at only 1/10 the cost, and can instantly explode with thousands of materials at key moments to seize traffic peaks.

 

Short video e-commerce has entered the era of computing power

Worrying about AI material overlap is actually unnecessary. Truly efficient platforms can automatically generate dozens of different scenarios, skin tones, and emotional versions for the same product. More important than perfect faces is localized voices—authentic London accents or Texas drawls can instantly close the psychological distance with users. AI makes the cost of trial and error almost zero, and the final competition is about who can run standardized production processes faster.

Speed is the new moat. Now, victory is no longer about team size or equipment investment, but about who can use systematic methods to turn user insights into thousands of materials faster, occupying user mindshare before competitors can react.